Therefore, there is a physical explanation of the difference in distributions of binding kinetics: Above the characteristic transition temperature Tc, there are multiple parallel kinetic paths, each experiencing certain barriers (barrier has a normal distribution ), resulting in log-normal kinetics (seen also in protein conformational dynamics simulations [31]).

Microscopic Atomic Binding Model and Simulation Results

We see that the logarithm of equilibrium constant can be fitted well with a normal distribution near the mean while near the tail can be fitted well with a exponential distribution.

Microscopic Atomic Binding Model and Simulation Results

We also see that the distribution of the intrinsic specificity can be fitted well with the normal distribution near the mean and also well fitted with the exponential distribution at the tail.

A random phenotype is first generated for the reference data, drawing from the standard normal distribution .

Gene-set analysis

This yields a roughly normally distributed variable Z with elements zg that reflects the strength of the association each gene has with the phenotype, with higher values corresponding to stronger associations.

Gene-set analysis

Evaluating fig 2 0 against the alternative fig > 0 yields a self-contained test, since under the self-contained null hypothesis that none of the genes is associated with the phenotype zg has a standard normal distribution for every gene g. Competitive gene-set analysis tests whether the genes in a gene-set are more strongly associated with the phenotype of interest than other genes.

Gene-set analysis

One complication that arises in this gene-level regression framework is that the standard linear regression model assumes that the error terms have independent normal distributions , i.e.

A: Flexibility values normalized distribution for all the yeast chromosomes.

Statistical analysis

It can be used to determine if two sets of data are significantly different from each other, and is most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known.

To demonstrate the effect that a priori trust in different modeling assumptions can have on the posterior estimates, we consider the case where the best, most likely and worst case scenarios for each of the two varied parameters corresponds to percentile 2.5, 50 and 97.5, respectively, of a normal distribution .

However, Kendall's tau is not appropriate for this type of data because Kendall's tau typically requires the underlying population to be bivariate normal; the frequencies we observe are not normally distributed .

The pooled proportion PP _PS ><N5+Pf ><Nf

From these quantities, a Z-score and p-Value can be computed assuming a normal distribution using Z = (PfPs)/SE.

Two-sample proportion test

While the distribution of PS and Pf are not necessarily normal, the distribution of (PfPS) is normally distributed .